Network-AI

分类: 工具与效率 | 上传者: Jovancoding | 下载: 0 | 版本: v1.0（最新）

本地 Python 编排技能：通过共享黑板文件、权限门控、令牌预算脚本和持久项目上下文的多代理工作流程。捆绑的 Python 脚本不进行网络调用，并且第三方依赖项为零。通过主机平台的session_send的工作流委托可以调用外部模型API。

更新日志: Source: GitHub https://github.com/Jovancoding/Network-AI

目录结构

当前层级: tree/main/

📁 .github/
- 📁 codeql/
  - 📄 codeql-config.yml 223 B
- 📁 ISSUE_TEMPLATE/
  - 📄 bug_report.md 633 B
  - 📄 feature_request.md 826 B
  - 📄 security.md 910 B
- 📁 workflows/
  - 📄 ci.yml 3.1 KB
  - 📄 codeql.yml 1.2 KB
  - 📄 dependabot-auto-merge.yml 657 B
  - 📄 scorecard.yml 1.1 KB
- 📄 copilot-instructions.md 2.3 KB
- 📄 dependabot.yml 962 B
- 📄 FUNDING.yml 21 B
- 📄 PULL_REQUEST_TEMPLATE.md 1002 B
- 📄 SECURITY.md 6.1 KB
📁 adapters/
- 📄 a2a-adapter.ts 12.4 KB
- 📄 adapter-registry.ts 13.7 KB
- 📄 agno-adapter.ts 7.8 KB
- 📄 aps-adapter.ts 12.0 KB
- 📄 autogen-adapter.ts 5.9 KB
- 📄 base-adapter.ts 3.2 KB
- 📄 codex-adapter.ts 14.2 KB
- 📄 crewai-adapter.ts 6.0 KB
- 📄 custom-adapter.ts 6.0 KB
- 📄 custom-streaming-adapter.ts 7.0 KB
- 📄 dspy-adapter.ts 6.0 KB
- 📄 haystack-adapter.ts 7.4 KB
- 📄 index.ts 4.3 KB
- 📄 langchain-adapter.ts 5.1 KB
- 📄 langchain-streaming-adapter.ts 6.4 KB
- 📄 llamaindex-adapter.ts 7.1 KB
- 📄 mcp-adapter.ts 7.9 KB
- 📄 minimax-adapter.ts 9.9 KB
- 📄 nemoclaw-adapter.ts 19.1 KB
- 📄 openai-assistants-adapter.ts 7.4 KB
- 📄 openclaw-adapter.ts 4.1 KB
- 📄 semantic-kernel-adapter.ts 6.3 KB
- 📄 streaming-base-adapter.ts 2.7 KB
📁 assets/
- 📄 demo.svg 20.5 KB
📁 bin/
- 📄 cli.ts 14.8 KB
- 📄 mcp-server.ts 12.0 KB
📁 docs/
- 📄 index.html 35.7 KB
📁 examples/
- 📄 01-hello-swarm.ts 8.2 KB
- 📄 02-fsm-pipeline.ts 9.3 KB
- 📄 03-parallel-agents.ts 9.1 KB
- 📄 05-code-review-swarm.ts 58.4 KB
- 📄 06-ai-pipeline-demo.ts 13.7 KB
- 📄 07-full-showcase.ts 64.9 KB
- 📄 08-control-plane-stress-demo.ts 7.9 KB
- 📄 09-real-langchain.ts 12.5 KB
- 📄 10-nemoclaw-sandbox-swarm.ts 12.5 KB
- 📄 demo-runner.ts 4.0 KB
- 📄 README.md 5.1 KB
📁 lib/
- 📄 blackboard-backend-crdt.ts 8.9 KB
- 📄 blackboard-backend-redis.ts 9.4 KB
- 📄 blackboard-backend.ts 5.9 KB
- 📄 blackboard-validator.ts 32.0 KB
- 📄 compliance-monitor.ts 11.5 KB
- 📄 consistency.ts 10.9 KB
- 📄 crdt.ts 5.4 KB
- 📄 errors.ts 5.7 KB
- 📄 federated-budget.ts 12.0 KB
- 📄 fsm-journey.ts 20.3 KB
- 📄 locked-blackboard.ts 33.1 KB
- 📄 logger.ts 5.2 KB
- 📄 mcp-blackboard-tools.ts 11.9 KB
- 📄 mcp-bridge.ts 17.1 KB
- 📄 mcp-tools-control.ts 16.1 KB
- 📄 mcp-tools-extended.ts 17.3 KB
- 📄 mcp-transport-sse.ts 17.9 KB
- 📄 qa-orchestrator.ts 15.4 KB
- 📄 swarm-utils.ts 18.2 KB
📁 references/
- 📄 adapter-system.md 12.5 KB
- 📄 auth-guardian.md 7.7 KB
- 📄 blackboard-schema.md 5.6 KB
- 📄 mcp-roadmap.md 14.3 KB
- 📄 trust-levels.md 5.7 KB
📁 scripts/
- 📄 blackboard.py 31.0 KB
- 📄 check_permission.py 23.6 KB
- 📄 context_manager.py 12.2 KB
- 📄 revoke_token.py 7.7 KB
- 📄 swarm_guard.py 44.9 KB
- 📄 validate_token.py 2.9 KB
📁 types/
- 📄 agent-adapter.d.ts 7.1 KB
- 📄 openclaw-core.d.ts 1.1 KB
- 📄 streaming-adapter.d.ts 2.2 KB
📄 .clawhubignore 1.1 KB
📄 .env.example 218 B
📄 .gitignore 504 B
📄 .npmignore 456 B
📄 ADOPTERS.md 1.2 KB
📄 ARCHITECTURE.md 12.8 KB
📄 AUDIT_LOG_SCHEMA.md 6.9 KB
📄 AWESOME_LISTS.md 4.6 KB
📄 BENCHMARKS.md 6.6 KB
📄 CHANGELOG.md 81.4 KB
📄 claude-project-prompt.md 5.2 KB
📄 claude-tools.json 7.1 KB
📄 CLAUDE.md 4.2 KB
📄 CODE_OF_CONDUCT.md 1.1 KB
📄 CODEX.md 3.5 KB
📄 CONTRIBUTING.md 2.6 KB
📄 Dockerfile 1.1 KB
📄 ENTERPRISE.md 6.8 KB
📄 glama.json 102 B
📄 index.ts 92.6 KB
📄 INTEGRATION_GUIDE.md 20.3 KB
📄 LICENSE 1.0 KB
📄 openapi.yaml 12.1 KB
📄 package-lock.json 9.8 KB
📄 package.json 3.6 KB
📄 QUICKSTART.md 16.8 KB
📄 README.md 23.6 KB
📄 requirements.txt 876 B
📄 run-tests.ts 2.7 KB
📄 run.ts 4.4 KB
📄 SECURITY.md 6.1 KB
📄 security.ts 31.4 KB
📄 setup.ts 15.5 KB
📄 SHOW_HN.md 3.8 KB
📄 skill.json 9.7 KB
📄 SKILL.md 26.0 KB
📄 socket.json 8.9 KB
📄 test-a2a.ts 15.5 KB
📄 test-adapters.ts 54.4 KB
📄 test-ai-quality.ts 5.8 KB
📄 test-cli.ts 15.0 KB
📄 test-codex.ts 14.3 KB
📄 test-minimax.ts 15.0 KB
📄 test-nemoclaw.ts 24.5 KB
📄 test-phase4.ts 30.4 KB
📄 test-phase5.ts 11.8 KB
📄 test-phase5b.ts 14.2 KB
📄 test-phase5c.ts 16.2 KB
📄 test-phase5d.ts 30.4 KB
📄 test-phase5e.ts 25.6 KB
📄 test-phase5f.ts 25.8 KB
📄 test-phase5g.ts 33.3 KB
📄 test-phase6.ts 31.1 KB
📄 test-priority.ts 21.8 KB
📄 test-qa.ts 18.2 KB
📄 test-security.ts 22.1 KB
📄 test-standalone.ts 59.2 KB
📄 test-streaming.ts 13.5 KB
📄 test.ts 19.0 KB
📄 tsconfig.build.json 138 B
📄 tsconfig.json 853 B

SKILL.md

---
name: Network-AI
description: "Local Python orchestration skill: multi-agent workflows via shared blackboard file, permission gating, token budget scripts, and persistent project context. The bundled Python scripts make no network calls and have zero third-party dependencies. Workflow delegations via the host platform's sessions_send may invoke external model APIs."
metadata:
  openclaw:
    emoji: "\U0001F41D"
    homepage: https://network-ai.org
    bundle_scope: "Python scripts only (scripts/*.py). All execution is local. Only Python stdlib — no other runtimes, adapters, or CLI tools are included."
    network_calls: "none — bundled scripts make zero network calls. The host platform's sessions_send (not part of this skill) may invoke external models."
    sessions_send: "NOT implemented or invoked by this skill. sessions_send is a host-platform built-in. This skill only provides budget guards that run before the platform delegates."
    sessions_ops: "platform-provided — outside this skill's control"
    requires:
      bins:
        - python3
      optional_bins: []
    env: {}
    privacy:
      audit_log:
        path: data/audit_log.jsonl
        scope: local-only
        description: "Local append-only JSONL file recording operation metadata. No data leaves the machine."
        pii_warning: "Do not include PII, secrets, or credentials in justification fields. Log entries persist on disk."
      data_directory:
        path: data/
        scope: local-only
        files: ["audit_log.jsonl", "active_grants.json", "project-context.json"]
        description: "All persistent state is local-only. No files are transmitted over the network."
---

# Swarm Orchestrator Skill

> **Scope:** The bundled Python scripts (`scripts/*.py`) make **no network calls**, use only the Python standard library, and have **zero third-party dependencies**. Tokens are UUID-based (`grant_{uuid4().hex}`) stored in `data/active_grants.json`. Audit logging is plain JSONL (`data/audit_log.jsonl`).

> **Data-flow notice:** This skill does NOT implement, invoke, or control `sessions_send`. That is a host-platform built-in (OpenClaw runtime). The orchestration instructions below describe *when* to call the platform's `sessions_send` after budget checks pass — but the actual network call, model endpoint, and data transmission are entirely the host platform's responsibility. If you need to prevent external network calls, disable or reroute `sessions_send` in your platform settings before installing this skill.

> **PII / sensitive-data warning:** The `justification` field in permission requests and the audit log (`data/audit_log.jsonl`) store free-text strings provided by agents. **Do not include PII, secrets, or credentials in justification text.** Consider restricting file permissions on `data/` or running this skill in an isolated workspace.

## Setup

**No pip install required.** All 6 scripts use Python standard library only — zero third-party packages.

> **Note on `requirements.txt`:** The file exists for documentation purposes only — it lists the stdlib modules used and has **no required packages**. All listed deps are commented out as optional. You do not need to run `pip install -r requirements.txt`.

```bash
# Prerequisite: python3 (any version ≥ 3.8)
python3 --version

# That's it. Run any script directly:
python3 scripts/blackboard.py list
python3 scripts/swarm_guard.py budget-init --task-id "task_001" --budget 10000

# Optional: for cross-platform file locking on Windows production hosts
pip install filelock  # only needed if you see locking issues on Windows
```

The `data/` directory is created automatically on first run. No configuration files, environment variables, or credentials are required.

Multi-agent coordination system for complex workflows requiring task delegation, parallel execution, and permission-controlled access to sensitive APIs.

## 🎯 Orchestrator System Instructions

**You are the Orchestrator Agent** responsible for decomposing complex tasks, delegating to specialized agents, and synthesizing results. Follow this protocol:

### Core Responsibilities

1. **DECOMPOSE** complex prompts into 3 specialized sub-tasks
2. **DELEGATE** using the budget-aware handoff protocol
3. **VERIFY** results on the blackboard before committing
4. **SYNTHESIZE** final output only after all validations pass

### Task Decomposition Protocol

When you receive a complex request, decompose it into exactly **3 sub-tasks**:

```
┌─────────────────────────────────────────────────────────────────┐
│                     COMPLEX USER REQUEST                        │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│  SUB-TASK 1   │   │  SUB-TASK 2   │   │  SUB-TASK 3   │
│ data_analyst  │   │ risk_assessor │   │strategy_advisor│
│    (DATA)     │   │   (VERIFY)    │   │  (RECOMMEND)  │
└───────────────┘   └───────────────┘   └───────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              ▼
                    ┌───────────────┐
                    │  SYNTHESIZE   │
                    │ orchestrator  │
                    └───────────────┘
```

**Decomposition Template:**
```
TASK DECOMPOSITION for: "{user_request}"

Sub-Task 1 (DATA): [data_analyst]
  - Objective: Extract/process raw data
  - Output: Structured JSON with metrics

Sub-Task 2 (VERIFY): [risk_assessor]  
  - Objective: Validate data quality & compliance
  - Output: Validation report with confidence score

Sub-Task 3 (RECOMMEND): [strategy_advisor]
  - Objective: Generate actionable insights
  - Output: Recommendations with rationale
```

### Budget-Aware Handoff Protocol

**CRITICAL:** Before EVERY `sessions_send`, call the handoff interceptor:

```bash
# ALWAYS run this BEFORE sessions_send
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze Q4 revenue data"
```

**Decision Logic:**
```
IF result.allowed == true:
    → Proceed with sessions_send
    → Note tokens_spent and remaining_budget
ELSE:
    → STOP - Do NOT call sessions_send
    → Report blocked reason to user
    → Consider: reduce scope or abort task
```

### Pre-Commit Verification Workflow

Before returning final results to the user:

```bash
# Step 1: Check all sub-task results on blackboard
python {baseDir}/scripts/blackboard.py read "task:001:data_analyst"
python {baseDir}/scripts/blackboard.py read "task:001:risk_assessor"
python {baseDir}/scripts/blackboard.py read "task:001:strategy_advisor"

# Step 2: Validate each result
python {baseDir}/scripts/swarm_guard.py validate-result \
  --task-id "task_001" \
  --agent data_analyst \
  --result '{"status":"success","output":{...},"confidence":0.85}'

# Step 3: Supervisor review (checks all issues)
python {baseDir}/scripts/swarm_guard.py supervisor-review --task-id "task_001"

# Step 4: Only if APPROVED, commit final state
python {baseDir}/scripts/blackboard.py write "task:001:final" \
  '{"status":"SUCCESS","output":{...}}'
```

**Verdict Handling:**
| Verdict | Action |
|---------|--------|
| `APPROVED` | Commit and return results to user |
| `WARNING` | Review issues, fix if possible, then commit |
| `BLOCKED` | Do NOT return results. Report failure. |

---

## The 3-Layer Memory Model

Every agent in the swarm operates with three memory layers, each with a different scope and lifetime:

| Layer | Name | Lifetime | Managed by |
|-------|------|----------|------------|
| **1** | Agent context | Ephemeral — current task only | Platform (per-session) |
| **2** | Blackboard | TTL-scoped — shared across agents | `scripts/blackboard.py` |
| **3** | Project context | Persistent — survives all sessions | `scripts/context_manager.py` |

### Layer 1 — Agent Context
Each agent's own context window: the current task instructions, conversation history, and immediate working memory. Managed automatically by the OpenClaw/LLM platform. Nothing to configure.

### Layer 2 — Blackboard (Shared Coordination State)
A shared markdown file (`swarm-blackboard.md`) for real-time cross-agent coordination: task results, grant tokens, status flags, and TTL-scoped cache entries. Agents read and write via `scripts/blackboard.py`. Entries expire automatically.

### Layer 3 — Project Context (Persistent Long-Term Memory)
A JSON file (`data/project-context.json`) that holds information every agent should know, regardless of what session or task is running:
- **Goals** — long-term objectives of the project
- **Tech stack** — languages, frameworks, infrastructure
- **Milestones** — completed, in-progress, and planned work
- **Architecture decisions** — design choices and their rationales
- **Banned approaches** — approaches that have been ruled out

#### Initialising Project Context

```bash
python {baseDir}/scripts/context_manager.py init \
  --name "MyProject" \
  --description "Multi-agent workflow automation" \
  --version "1.0.0"
```

#### Injecting Context into an Agent System Prompt

```bash
python {baseDir}/scripts/context_manager.py inject
```

Copy the output block to the top of your agent's system prompt. Every agent that receives this block shares the same long-term project awareness.

#### Recording a Decision

```bash
python {baseDir}/scripts/context_manager.py update \
  --section decisions \
  --add '{"decision": "Use atomic blackboard commits", "rationale": "Prevent race conditions in parallel agents"}'
```

#### Updating Milestones

```bash
# Mark a milestone complete
python {baseDir}/scripts/context_manager.py update \
  --section milestones --complete "Ship v2.0"

# Add a planned milestone
python {baseDir}/scripts/context_manager.py update \
  --section milestones --add '{"planned": "Integrate vector memory"}'
```

#### Setting the Tech Stack

```bash
python {baseDir}/scripts/context_manager.py update \
  --section stack \
  --set '{"language": "Python", "runtime": "Python 3.11", "framework": "SwarmOrchestrator"}'
```

#### Banning an Approach

```bash
python {baseDir}/scripts/context_manager.py update \
  --section banned \
  --add "Direct database writes from agent scripts (use permission gating)"
```

---

## When to Use This Skill

- **Task Delegation**: Route work to specialized agents (data_analyst, strategy_advisor, risk_assessor)
- **Parallel Execution**: Run multiple agents simultaneously and synthesize results
- **Permission Wall**: Gate access to DATABASE, PAYMENTS, EMAIL, or FILE_EXPORT operations (abstract local resource types — no external credentials required)
- **Shared Blackboard**: Coordinate agent state via persistent markdown file

## Quick Start

### 1. Initialize Budget (FIRST!)

**Always initialize a budget before any multi-agent task:**

```bash
python {baseDir}/scripts/swarm_guard.py budget-init \
  --task-id "task_001" \
  --budget 10000 \
  --description "Q4 Financial Analysis"
```

### 2. Delegate a Task to Another Session

> **Platform note:** `sessions_list`, `sessions_send`, and `sessions_history` are **OpenClaw host platform built-ins** — they are part of the OpenClaw runtime, not provided or invoked by this skill's Python scripts. This skill only runs local `python scripts/*.py` commands. The guidance below describes how to combine the platform's session tools with this skill's budget guard.

First check budget, then use the OpenClaw platform operation:

```bash
# 1. Check budget (this skill's Python script)
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" --from orchestrator --to data_analyst \
  --message "Analyze Q4 revenue data"

# 2. If allowed, delegate using the OpenClaw platform tool (not this skill):
#    sessions_list    → see available sessions/agents
#    sessions_send    → send task to another session
#    sessions_history → check results from delegated work
```

**Example delegation prompt:**
```
After running swarm_guard.py intercept-handoff and getting result.allowed == true,
use the OpenClaw sessions_send platform tool to ask the data_analyst session:
"Analyze Q4 revenue trends from the SAP export data and summarize key insights"
```

### 3. Check Permission Before API Access

Before accessing SAP or Financial APIs, evaluate the request:

```bash
# Run the permission checker script
python {baseDir}/scripts/check_permission.py \
  --agent "data_analyst" \
  --resource "DATABASE" \
  --justification "Need Q4 invoice data for quarterly report" \
  --scope "read:invoices"
```

The script will output a grant token if approved, or denial reason if rejected.

### 4. Use the Shared Blackboard

Read/write coordination state:

```bash
# Write to blackboard
python {baseDir}/scripts/blackboard.py write "task:q4_analysis" '{"status": "in_progress", "agent": "data_analyst"}'

# Read from blackboard  
python {baseDir}/scripts/blackboard.py read "task:q4_analysis"

# List all entries
python {baseDir}/scripts/blackboard.py list
```

## Agent-to-Agent Handoff Protocol

When delegating tasks between agents/sessions:

### Step 1: Initialize Budget & Check Capacity
```bash
# Initialize budget (if not already done)
python {baseDir}/scripts/swarm_guard.py budget-init --task-id "task_001" --budget 10000

# Check current status
python {baseDir}/scripts/swarm_guard.py budget-check --task-id "task_001"
```

### Step 2: Identify Target Agent

> **Platform note:** `sessions_list` is an **OpenClaw host platform built-in**, not provided by this skill.

```
sessions_list  # OpenClaw platform operation — find available agents
```

Common agent types:
| Agent | Specialty |
|-------|-----------|
| `data_analyst` | Data processing, SQL, analytics |
| `strategy_advisor` | Business strategy, recommendations |
| `risk_assessor` | Risk analysis, compliance checks |
| `orchestrator` | Coordination, task decomposition |

### Step 3: Intercept Before Handoff (REQUIRED)

```bash
# This checks budget AND handoff limits before allowing the call
python {baseDir}/scripts/swarm_guard.py intercept-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze Q4 data" \
  --artifact  # Include if expecting output
```

**If ALLOWED:** Proceed to Step 4
**If BLOCKED:** Stop - do not call sessions_send

### Step 4: Construct Handoff Message

Include these fields in your delegation:
- **instruction**: Clear task description
- **context**: Relevant background information
- **constraints**: Any limitations or requirements
- **expectedOutput**: What format/content you need back

### Step 5: Send via OpenClaw Platform Session Tool

> **Platform note:** `sessions_send` is an **OpenClaw host platform built-in** — it is NOT implemented by this skill. This skill only provides the budget guard (`swarm_guard.py`) that must be run first.

```
# OpenClaw platform operation (not this skill):
sessions_send to data_analyst:
"[HANDOFF]
Instruction: Analyze Q4 revenue by product category
Context: Using SAP export from ./data/q4_export.csv
Constraints: Focus on top 5 categories only
Expected Output: JSON summary with category, revenue, growth_pct
[/HANDOFF]"
```

### Step 6: Check Results

> **Platform note:** `sessions_history` is an **OpenClaw host platform built-in**, not provided by this skill.

```
sessions_history data_analyst  # OpenClaw platform operation — get the response
```

## Permission Wall

**CRITICAL**: Always check permissions before accessing:
- `DATABASE` - Internal database / data store access
- `PAYMENTS` - Financial/payment data services
- `EMAIL` - Email sending capability
- `FILE_EXPORT` - Exporting data to local files

> **Note**: These are abstract local resource type names used by `check_permission.py`. No external API credentials are required or used — all permission evaluation runs locally.

### Permission Evaluation Criteria

| Factor | Weight | Criteria |
|--------|--------|----------|
| Justification | 40% | Must explain specific task need |
| Trust Level | 30% | Agent's established trust score |
| Risk Assessment | 30% | Resource sensitivity + scope breadth |

### Using the Permission Script

```bash
# Request permission
python {baseDir}/scripts/check_permission.py \
  --agent "your_agent_id" \
  --resource "PAYMENTS" \
  --justification "Generating quarterly financial summary for board presentation" \
  --scope "read:revenue,read:expenses"

# Output if approved:
# ✅ GRANTED
# Token: grant_a1b2c3d4e5f6
# Expires: 2026-02-04T15:30:00Z
# Restrictions: read_only, no_pii_fields, audit_required

# Output if denied:
# ❌ DENIED
# Reason: Justification is insufficient. Please provide specific task context.
```

### Restriction Types

| Resource | Default Restrictions |
|----------|---------------------|
| DATABASE | `read_only`, `max_records:100` |
| PAYMENTS | `read_only`, `no_pii_fields`, `audit_required` |
| EMAIL | `rate_limit:10_per_minute` |
| FILE_EXPORT | `anonymize_pii`, `local_only` |

## Shared Blackboard Pattern

The blackboard (`swarm-blackboard.md`) is a markdown file for agent coordination:

```markdown
# Swarm Blackboard
Last Updated: 2026-02-04T10:30:00Z

## Knowledge Cache
### task:q4_analysis
{"status": "completed", "result": {...}, "agent": "data_analyst"}

### cache:revenue_summary  
{"q4_total": 1250000, "growth": 0.15}
```

### Blackboard Operations

```bash
# Write with TTL (expires after 1 hour)
python {baseDir}/scripts/blackboard.py write "cache:temp_data" '{"value": 123}' --ttl 3600

# Read (returns null if expired)
python {baseDir}/scripts/blackboard.py read "cache:temp_data"

# Delete
python {baseDir}/scripts/blackboard.py delete "cache:temp_data"

# Get full snapshot
python {baseDir}/scripts/blackboard.py snapshot
```

## Parallel Execution

For tasks requiring multiple agent perspectives:

### Strategy 1: Merge (Default)
Combine all agent outputs into unified result.
```
Ask data_analyst AND strategy_advisor to both analyze the dataset.
Merge their insights into a comprehensive report.
```

### Strategy 2: Vote
Use when you need consensus - pick the result with highest confidence.

### Strategy 3: First-Success
Use for redundancy - take first successful result.

### Strategy 4: Chain
Sequential processing - output of one feeds into next.

### Example Parallel Workflow

> **Platform note:** `sessions_send` and `sessions_history` are **OpenClaw host platform built-ins**, not provided by this skill. This skill provides only the `swarm_guard.py` budget/handoff check that runs before each delegation.

```
# For each delegation below, first run:
#   python {baseDir}/scripts/swarm_guard.py intercept-handoff --task-id "task_001" --from orchestrator --to <agent> --message "<task>"
# Then, if allowed, use the OpenClaw platform tool:
1. sessions_send to data_analyst: "Extract key metrics from Q4 data"
2. sessions_send to risk_assessor: "Identify compliance risks in Q4 data"
3. sessions_send to strategy_advisor: "Recommend actions based on Q4 trends"
4. Wait for all responses via sessions_history
5. Synthesize: Combine metrics + risks + recommendations into executive summary
```

## Security Considerations

1. **Never bypass the permission wall** for gated resources
2. **Always include justification** explaining the business need
3. **Use minimal scope** - request only what you need
4. **Check token expiry** - tokens are valid for 5 minutes
5. **Validate tokens** - use `python {baseDir}/scripts/validate_token.py TOKEN` to verify grant tokens before use
6. **Audit trail** - all permission requests are logged

## 📝 Audit Trail Requirements (MANDATORY)

**Every sensitive action MUST be logged to `data/audit_log.jsonl`** to maintain compliance and enable forensic analysis.

> **Privacy note:** Audit log entries contain agent-provided free-text fields (justifications, descriptions). These are stored locally in `data/audit_log.jsonl` and never transmitted over the network by this skill. However, **do not put PII, passwords, or API keys in justification strings** — they persist on disk. Consider periodic log rotation and restricting OS file permissions on the `data/` directory.

### What Gets Logged Automatically

The scripts automatically log these events:
- `permission_granted` - When access is approved
- `permission_denied` - When access is rejected
- `permission_revoked` - When a token is manually revoked
- `ttl_cleanup` - When expired tokens are purged
- `result_validated` / `result_rejected` - Swarm Guard validations

### Log Entry Format

```json
{
  "timestamp": "2026-02-04T10:30:00+00:00",
  "action": "permission_granted",
  "details": {
    "agent_id": "data_analyst",
    "resource_type": "DATABASE",
    "justification": "Q4 revenue analysis",
    "token": "grant_abc123...",
    "restrictions": ["read_only", "max_records:100"]
  }
}
```

### Reading the Audit Log

```bash
# View recent entries (last 10)
tail -10 {baseDir}/data/audit_log.jsonl

# Search for specific agent
grep "data_analyst" {baseDir}/data/audit_log.jsonl

# Count actions by type
cat {baseDir}/data/audit_log.jsonl | jq -r '.action' | sort | uniq -c
```

### Custom Audit Entries

If you perform a sensitive action manually, log it:

```python
import json
from datetime import datetime, timezone
from pathlib import Path

audit_file = Path("{baseDir}/data/audit_log.jsonl")
entry = {
    "timestamp": datetime.now(timezone.utc).isoformat(),
    "action": "manual_data_access",
    "details": {
        "agent": "orchestrator",
        "description": "Direct database query for debugging",
        "justification": "Investigating data sync issue #1234"
    }
}
with open(audit_file, "a") as f:
    f.write(json.dumps(entry) + "\n")
```

## 🧹 TTL Enforcement (Token Lifecycle)

Expired permission tokens are automatically tracked. Run periodic cleanup:

```bash
# Validate a grant token
python {baseDir}/scripts/validate_token.py grant_a1b2c3d4e5f6

# List expired tokens (without removing)
python {baseDir}/scripts/revoke_token.py --list-expired

# Remove all expired tokens
python {baseDir}/scripts/revoke_token.py --cleanup

# Output:
# 🧹 TTL Cleanup Complete
#    Removed: 3 expired token(s)
#    Remaining active grants: 2
```

**Best Practice**: Run `--cleanup` at the start of each multi-agent task to ensure a clean permission state.

## ⚠️ Swarm Guard: Preventing Common Failures

Two critical issues can derail multi-agent swarms:

### 1. The Handoff Tax 💸

**Problem**: Agents waste tokens "talking about" work instead of doing it.

**Prevention**:
```bash
# Before each handoff, check your budget:
python {baseDir}/scripts/swarm_guard.py check-handoff --task-id "task_001"

# Output:
# 🟢 Task: task_001
#    Handoffs: 1/3
#    Remaining: 2
#    Action Ratio: 100%
```

**Rules enforced**:
- **Max 3 handoffs per task** - After 3, produce output or abort
- **Max 500 chars per message** - Be concise: instruction + constraints + expected output
- **60% action ratio** - At least 60% of handoffs must produce artifacts
- **2-minute planning limit** - No output after 2min = timeout

```bash
# Record a handoff (with tax checking):
python {baseDir}/scripts/swarm_guard.py record-handoff \
  --task-id "task_001" \
  --from orchestrator \
  --to data_analyst \
  --message "Analyze sales data, output JSON summary" \
  --artifact  # Include if this handoff produces output
```

### 2. Silent Failure Detection 👻

**Problem**: One agent fails silently, others keep working on bad data.

**Prevention - Heartbeats**:
```bash
# Agents must send heartbeats while working:
python {baseDir}/scripts/swarm_guard.py heartbeat --agent data_analyst --task-id "task_001"

# Check if an agent is healthy:
python {baseDir}/scripts/swarm_guard.py health-check --agent data_analyst

# Output if healthy:
# 💚 Agent 'data_analyst' is HEALTHY
#    Last seen: 15s ago

# Output if failed:
# 💔 Agent 'data_analyst' is UNHEALTHY
#    Reason: STALE_HEARTBEAT
#    → Do NOT use any pending results from this agent.
```

**Prevention - Result Validation**:
```bash
# Before using another agent's result, validate it:
python {baseDir}/scripts/swarm_guard.py validate-result \
  --task-id "task_001" \
  --agent data_analyst \
  --result '{"status": "success", "output": {"revenue": 125000}, "confidence": 0.85}'

# Output:
# ✅ RESULT VALID
#    → APPROVED - Result can be used by other agents
```

**Required result fields**: `status`, `output`, `confidence`

### Supervisor Review

Before finalizing any task, run supervisor review:
```bash
python {baseDir}/scripts/swarm_guard.py supervisor-review --task-id "task_001"

# Output:
# ✅ SUPERVISOR VERDICT: APPROVED
#    Task: task_001
#    Age: 1.5 minutes
#    Handoffs: 2
#    Artifacts: 2
```

**Verdicts**:
- `APPROVED` - Task healthy, results usable
- `WARNING` - Issues detected, review recommended
- `BLOCKED` - Critical failures, do NOT use results

## Troubleshooting

### Permission Denied
- Provide more specific justification (mention task, purpose, expected outcome)
- Narrow the requested scope
- Check agent trust level

### Blackboard Read Returns Null
- Entry may have expired (check TTL)
- Key may be misspelled
- Entry was never written

### Session Not Found
- Run `sessions_list` (OpenClaw platform built-in) to see available sessions
- Session may need to be started first

## References

This skill is part of the larger [Network-AI](https://github.com/Jovancoding/Network-AI) project. See the repository for full documentation on the permission system, blackboard schema, and trust-level calculations.

登录后下载/点赞/收藏 ❤ 29 | ★ 0

Network-AI

目录结构

SKILL.md

举报内容

提示